I have this data set:
x <- c(0, 40, 80, 120, 160, 200)
y <- c(6.52, 5.10, 4.43, 3.99, 3.75, 3.60)
I calculated a linear model using lm():
model <- lm(y ~ x)
I want know the predicted values of x if I have new y values, e.g. ynew <- c(5.5, 4.5, 3.5), but if I use the predict() function, it calculates only new y values.
How can I predict new x values if I have new y values?
If your relationship is nonmonotone or if you have multiple predictor values then there can be multiple x-values for a given y-value and you need to decide how to deal with that.
One option that could be slow (and may be the method used in the other packages mentioned) is to use the uniroot function:
x <- runif(100, min=-1,max=2)
y <- exp(x) + rnorm(100,0,0.2)
fit <- lm( y ~ poly(x,3), x=TRUE )
(tmp <- uniroot( function(x) predict(fit, data.frame(x=x)) - 4, c(-1, 2) )$root)
library(TeachingDemos)
plot(x,y)
Predict.Plot(fit, 'x', data=data.frame(x=x), add=TRUE, ref.val=tmp)
You could use the TkPredict function from the TeachingDemos package to eyeball a solution.
Or you could get a fairly quick approximation by generating a lot of predicted points, then feeding them to the approxfun or splinfun functions to produce the approximations:
tmpx <- seq(min(x), max(x), length.out=250)
tmpy <- predict(fit, data.frame(x=tmpx) )
tmpfun <- splinefun( tmpy, tmpx )
tmpfun(4)